Abstract:
The number of mitotic cells is an important metric in the diagnosis of breast cancer.
It gives the extent to which the tumor has spread, which has further implications in pre dicting cancer’s aggressiveness. Mitosis counting is a laborious and complex process done
manually by a pathologist by analyzing Hematoxylin and Eosin (H&E) stained biopsy slices
under a microscope. Detection of mitosis in H&E stained slices is difficult due to insufficient
datasets and the similarity between mitotic and non-mitotic cells. The advent of computer aided mitosis detection methods made the whole process far easier by helping in screening,
identifying and labeling mitotic cells. Traditional detection methods relied on image pro cessing techniques in which image features are distinguished using handcrafted features. The
development and application of neural networks have also been investigated in the mitosis
detection process due to their ability to extract features automatically. This project inves tigates mitosis detection as both a classification problem and an object detection problem.
The mitosis classification problem employed a multiCNN framework for feature extraction
and used machine learning techniques for classification. Tissue level detection of mitosis
was also investigated using pre-trained Faster R-CNN by taking raw images as in medical
perspective. The experiments were carried out on two publicly available datasets, MITOS ATYPIA-14 dataset and the latest TUPAC16 dataset and the results were compared with
other methods in the literature.
